Necessity and sufficiency have begun to receive explicit attention in the XAI literature. [1]} propose a bandit procedure for identifying a minimal set of Boolean conditions that entails a predictive outcome (more on this in Sect. ). [2]} propose an autoencoder for learning pertinent negatives and positives, i.e. features whose presence or absence is decisive for a given label, while [3]} develop a technique for generating symbolic corrections to alter model outputs. Both methods are optimized for neural networks, unlike the model-agnostic approaches we prioritize here.
